Systems and methods for attributing electronic purchase events to previous online and offline activity of the purchaser

ABSTRACT

Systems and methods are disclosed for attributing payment vehicle purchase events to previous activity of a purchaser. One method comprises: receiving purchase information associated with a purchase event by a purchaser; comparing the received purchase information to a profile data store to identify a purchaser profile associated with the purchaser, the profile data store comprising a plurality of purchaser profiles, wherein each purchaser profile comprises payment vehicle data and a tracking element; determining the tracking element of the identified purchaser profile; identifying one or more activities of the purchaser using the tracking element of the identified purchaser profile; for each of the identified one or more activities, assessing the strength of attributing the purchase event to the activity, using environmental and/or behavioral data associated with the tracking element; and determining whether to attribute the purchase event to one or more of the identified activities based on the assessment.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This patent application is a continuation of and claims the benefit ofpriority to U.S. application Ser. No. 15/382,069, filed Dec. 16, 2016,the entirety of which is incorporated herein by reference.

FIELD OF DISCLOSURE

The present disclosure relates generally to the field of field ofdetermining a correlation between online advertising campaigns or onlineactivity and subsequent purchase events.

BACKGROUND

Many companies utilize a variety of different marketing campaigns toattract new business, increase revenue, or serve other business needs.For example, it is not uncommon for companies to advertise throughmultiple marketing channels, such as TV, radio, Internet, and so forth.With specific regard to Internet-based or electronic marketing, it isoften difficult for these companies to correlate advertising andmarketing expenditures to subsequent purchase events, especially whenthose purchase events occur through different sales channels or occursubsequent to a consumer's initial exposure to marketing communications.Thus, there is a desire for systems and methods configured to bettercorrelate advertising and marketing expenditures to subsequent purchaseevents.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for attributing payment vehicle purchase events toprevious activity by the purchaser.

In one embodiment, a computer-implemented method is disclosed forattributing payment vehicle purchase events to previous activity of apurchaser. The method includes: receiving, by a profiler computingsystem, purchase information associated with a purchase event by apurchaser; comparing, by the profiler computing system, the receivedpurchase information to a profile data store to identify a purchaserprofile associated with the purchaser, the profile data store comprisinga plurality of purchaser profiles, wherein each purchaser profilecomprises payment vehicle data and a tracking element; determining, bythe profiler computing system, the tracking element of the identifiedpurchaser profile; identifying one or more activities of the purchaserusing the tracking element of the identified purchaser profile; for eachof the identified one or more activities, assessing the strength ofattributing the purchase event to the activity, using environmentaland/or behavioral data associated with the tracking element of theidentified purchaser profile; and determining, by the profiler computingsystem, whether to attribute the purchase event to one or more of theidentified activities of the purchaser based on the assessment.

In accordance with another embodiment, a system is disclosed forattributing payment vehicle purchase events to previous activity of apurchaser. The system comprises: a data storage device storinginstructions for attributing payment vehicle purchase events to previousactivity of the purchaser; and a profiler computing system configured toexecute the instructions to perform a method including: receivingpurchase information associated with a purchase event by a purchaser;comparing the received purchase information to a profile data store toidentify a purchaser profile associated with the purchaser, the profiledata store comprising a plurality of purchaser profiles, wherein eachpurchaser profile comprises payment vehicle data and a tracking element;determining the tracking element of the identified purchaser profile;identifying one or more activities of the purchaser using the trackingelement of the identified purchaser profile; for each of the identifiedone or more activities, assessing the strength of attributing thepurchase event to the activity, using environmental and/or behavioraldata associated with the tracking element of the identified purchaserprofile; and determining whether to attribute the purchase event to oneor more of the identified activities of the purchaser based on theassessment.

In accordance with another embodiment, a non-transitory machine-readablemedium stores instructions that, when executed by a profiler computingsystem, causes the profiler computing system to perform a method forattributing payment vehicle purchase events to previous activity of apurchaser. The method includes: receiving, by a profiler computingsystem, purchase information associated with a purchase event by apurchaser; comparing, by the profiler computing system, the receivedpurchase information to a profile data store to identify a purchaserprofile associated with the purchaser, the profile data store comprisesa plurality of purchaser profiles, wherein each purchaser profilecomprises payment vehicle data and a tracking element; determining, bythe profiler computing system, the tracking element of the identifiedpurchaser profile; identifying one or more activities of the purchaserusing the tracking element of the identified purchaser profile; for eachof the identified one or more activities, assessing the strength ofattributing the purchase event to the activity, using environmentaland/or behavioral data associated with the tracking element of theidentified purchaser profile; and determining, by the profiler computingsystem, whether to attribute the purchase event to one or more of theidentified activities of the purchaser based on the assessment.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages on the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the detailed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

It is believed that certain embodiments will be better understood fromthe following description taken in conjunction with the accompanyingdrawings, in which like references indicate similar elements and inwhich:

FIG. 1 schematically depicts example linkages that can be determined inaccordance with various non-limiting embodiments.

FIGS. 2A-2D depict example system diagrams in accordance with onenon-limiting embodiment.

FIG. 3 depicts an example system diagram in which a receiving entity ofan attribution report provides targeted offers to purchasers;

FIG. 4 is an example message sequent chart in accordance with onenon-limiting embodiment; and

FIG. 5 depicts an example computing device.

FIG. 6 is a flow chart depicting an example process for attributing apurchase event with an identified activity.

DETAILED DESCRIPTION

Various non-limiting embodiments of the present disclosure will now bedescribed to provide an overall understanding of the principles of thestructure, function, and use of systems and methods disclosed herein.One or more examples of these non-limiting embodiments are illustratedin the selected examples disclosed and described in detail withreference made to the figures in the accompanying drawings. Those ofordinary skill in the art will understand that systems and methodsspecifically described herein and illustrated in the accompanyingdrawings are non-limiting embodiments. The features illustrated ordescribed in connection with one non-limiting embodiment may be combinedwith the features of other non-limiting embodiments. Such modificationsand variations are intended to be included within the scope of thepresent disclosure.

The systems, apparatuses, devices, and methods disclosed herein aredescribed in detail by way of examples and with reference to thefigures. The examples discussed herein are examples only and areprovided to assist in the explanation of the apparatuses, devices,systems, and methods described herein. None of the features orcomponents shown in the drawings or discussed below should be taken asmandatory for any specific implementation of any of these theapparatuses, devices, systems or methods unless specifically designatedas mandatory. In addition, elements illustrated in the figures are notnecessarily drawn to scale for simplicity and clarity of illustration.For ease of reading and clarity, certain components, modules, or methodsmay be described solely in connection with a specific figure. In thisdisclosure, any identification of specific techniques, arrangements,etc. are either related to a specific example presented or are merely ageneral description of such a technique, arrangement, etc.Identifications of specific details or examples are not intended to be,and should not be, construed as mandatory or limiting unlessspecifically designated as such. Any failure to specifically describe acombination or sub-combination of components should not be understood asan indication that any combination or sub-combination is not possible.It will be appreciated that modifications to disclosed and describedexamples, arrangements, configurations, components, elements,apparatuses, devices, systems, methods, etc. can be made and may bedesired for a specific application. Also, for any methods described,regardless of whether the method is described in conjunction with a flowdiagram, it should be understood that unless otherwise specified orrequired by context, any explicit or implicit ordering of stepsperformed in the execution of a method does not imply that those stepsmust be performed in the order presented but instead may be performed ina different order or in parallel.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” “some example embodiments,” “one exampleembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with any embodimentis included in at least one embodiment. Thus, appearances of the phrases“in various embodiments,” “in some embodiments,” “in one embodiment,”“some example embodiments,” “one example embodiment,” or “in anembodiment” in places throughout the specification are not necessarilyall referring to the same embodiment. Furthermore, the particularfeatures, structures, or characteristics may be combined in any suitablemanner in one or more embodiments.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules can be implemented in software,hardware, or a combination of software and hardware. The term “software”is used expansively to include not only executable code, for examplemachine-executable or machine-interpretable instructions, but also datastructures, data stores and computing instructions stored in anysuitable electronic format, including firmware, and embedded software.The terms “information” and “data” are used expansively and includes awide variety of electronic information, including executable code;content such as text, video data, and audio data, among others; andvarious codes or flags. The terms “information,” “data,” and “content”are sometimes used interchangeably when permitted by context. It shouldbe noted that although for clarity and to aid in understanding someexamples discussed herein might describe specific features or functionsas part of a specific component or module, or as occurring at a specificlayer of a computing device (for example, a hardware layer, operatingsystem layer, or application layer), those features or functions may beimplemented as part of a different component or module or operated at adifferent layer of a communication protocol stack. Those of ordinaryskill in the art will recognize that the systems, apparatuses, devices,and methods described herein can be applied to, or easily modified foruse with, other types of equipment, can use other arrangements ofcomputing systems such as client server distributed systems, and can useother protocols, or operate at other layers in communication protocolstacks, than are described.

Many companies utilize a variety of different marketing campaigns toattract new business, increase revenue, or serve other business needs.Many companies engage in advertising through multiple marketingchannels, such as TV, radio, Internet, and so forth. With specificregard to Internet-based marketing, it is often difficult for thesecompanies to correlate advertising and marketing expenditures tosubsequent purchase events, especially when those purchase events occurthrough different sales channels or occur subsequent to a consumer'sinitial exposure to marketing communications. By way of example, atypical consumer may spend time visiting or otherwise interacting with amerchant's website to research a particular good and/or service,referred to herein as “product.” The consumer may arrive at themerchant's website through “clicking through” an advertisement appearingon a webpage or within a mobile application, through keyword searching,or through other means. The consumer may decide to the buy the productthough the merchant's ecommerce portal during that visit to themerchant's website. Such purchase events are generally traceable by themerchant, or an affiliated entity, such that a correlation between theconsumer's online activity and the purchase event can be tracked andidentified as a successful “conversion.” However, in other instances,instead of purchasing the product in an online transaction, the consumermay decide to physically travel to a brick-and-mortar retail location ofthe merchant and purchase the product. In some embodiments, a purchaseevent may refer to, e.g., a purchase activity related to a transactionor prospective transaction with a merchant and/or category of merchantsand/or a purchase activity relating to a transaction or prospectivetransaction over a good, service, and/or stock keeping unit.

Using presently available consumer tracking data, there may be adisconnect between the consumer's online activity and their subsequentpurchases at the brick-and-mortar retail location. Nevertheless, it maybe desirable for the merchant, or other interested parties, tounderstand any correlation that exists between the consumer's previousexposure to the merchant's website, or other online activities, and thesubsequent purchase of a product at the brick-and-mortar retaillocation.

In yet other instances, the consumer may decide to leave the merchant'swebsite and then return to the merchant's website at a later point intime to make the purchase. If the consumer arrived at the website thefirst time by “clicking through” an online advertisement, but arrived atthe website the second time through other means (e.g., keywordsearching, direct URL input, etc.), the correlation between the twoonline sessions may not be known to a merchant. More particularly, dueto techniques used for tracking online activity by marketers and otherdata aggregators, when the consumer returns to the website at a laterpoint in time to make a purchase, the online advertisements to which theconsumer previously interacted with do not typically get credited fordriving that purchase event. Accordingly, it is desirable for merchantsto know that a correlation exists between initial interaction withonline advertisements and/or other online activities and the subsequentpurchase of the product at an ecommerce website during a separate onlinesession.

The presently disclosed system and methods can generally provide acorrelation of online activity of a consumer to subsequent purchaseevents by that consumer. The subsequent purchase events can occur at anytype of merchant location, including online/ecommerce merchant locationsand brick-and-mortar retail locations. The online activity can includeexposure to marketing assets, advertisements, offers, coupons, websites,as well as online searching, and so forth. Such online activity can betracked and logged by a data aggregator computing system. In someembodiments, at least a portion of the data aggregator functionality isperformed by a third party service, such as Google®. Additionally oralternatively, in some embodiments, at least a portion of the dataaggregator functionality is performed by the merchant's web serversand/or servers including or providing data aggregation services.

In accordance with the systems and methods described herein, a profilefor the consumer can be generated and stored by a profiler computingsystem subsequent to the consumer making an online purchase. The onlinepurchase can be made through interactions with a networked user deviceor computing device (e.g., a laptop, a desktop computer, a smartappliance such as a smart television, a mobile phone, or any othermobile device, such as a tablet computer, and so forth). As described inmore detail below, during the online purchase event the purchaser canprovide transaction data (e.g., purchase information) to a financialtransaction services processor of a merchant, including payment vehicleinformation, over a network.

One example of a financial transaction services processor is Vantiv®.Additionally, other information can be provided to the financialtransaction services processor over the network during the course of thetransaction and, in some embodiments, can be provided after conclusionof the transaction. Such information can include a tracking elementassociated with the purchaser and/or the purchaser's networked userdevice. For example, in some embodiments, the tracking element can be adevice identifier of the purchaser's networked user device. This deviceidentifier can be used as part of the fraud prevention services of thefinancial transaction services processor or the payment networks (e.g.,Visa® or MasterCard®).

The device identifier can be, for example, one or more of a source IPaddress, a MAC address, a device fingerprint, a browser fingerprint, aunique identifier, or any other suitable type of identifiercorresponding to the networked user device (e.g., computing device) ofthe purchaser.

Additionally or alternatively, the tracking element can be an identifierassociated with the purchaser. For example, in some embodiments, thetracking element can be embodied as, or otherwise include, an emailaddress, a postal address, a phone number, a loyalty account number, ausername, and/or any other unique identifier associated with thepurchaser. Additionally, in accordance with the present disclosure, thefinancial transaction services processor can provide information fromthe online or initial purchase event to the profiler computing system.

As described in more detail below, the profiler computing system inaccordance with the present disclosure can store a profile for thepurchaser that generally links that tracking element (e.g., the deviceID or purchaser ID) to the payment vehicle used by the purchaser duringthe online purchase event. In some embodiments, more than one trackingelement can be linked to a particular purchaser. Furthermore, as is tobe appreciated, in view of PCI requirements, various tokenizationtechniques can be used to mask personally identifiable informationwithout departing from the scope of the present disclosure. In thisregard, if the profiler computing system links a tracking element to atoken, it is to be understood that the tracking element is stillconsidered to be linked to the payment vehicle. The profile computersystem can continue to augment the purchaser profile over time asadditional online transactions are made by the purchaser. For example,if the purchaser initiates a second transaction from the same networkeduser device using a second payment vehicle, the second payment vehiclecan be added to the purchaser's profile. In that way, the second paymentvehicle may be linked to the tracking element.

The purchaser can then make a purchase at a merchant location (e.g., abrick-and-mortar retail location). Subsequently, a financial transactionservices processor of the merchant can facilitate the communicationswith various payment networks (e.g., VISA® or MasterCard®), as may beneeded, to complete the purchase event. For example, the purchaser canprovide a payment vehicle to a point-of-sale system (POS) of themerchant. The POS can, in turn, communicate transactional information(e.g., subsequent purchase information) to the financial transactionservices processor. The transactional information can typically includetransaction amount, merchant identifier (MID), payment vehicle data,among other information. In accordance with the present disclosure, thefinancial transaction services processor can provide information fromthe subsequent purchase event to the profiler computing system. In oneembodiment, payment vehicle information is provided to the profilercomputing system so that the profile computer system can determine ifthere is an affiliated (e.g., associated) profile. If there is aprofile, the profiler computing system can determine the trackingelement, or tracking elements, that are linked to that payment vehicle.As described above, in some embodiments, this linkage can be establishedduring the purchaser's previous online purchases. Additionally oralternatively, the linkage may be established prior to the purchasermaking on online purchase. For example, in some embodiments, the linkagemay be established based at least in part on, or otherwise as a functionof, payment vehicle information maintained in a purchaser's mobilewallet prior to making an online purchase. In any event, using thetracking element and the data collected by the data aggregator computingsystem, it can be determined if the purchase event at the merchantlocation (e.g., the brick-and-mortar retail location) can be attributedto any previous online activity of the purchaser. Such attribution canbe based on, for example, the transactional information provided to thefinancial transaction services processor by the POS. For example, insome embodiments, the attribution can be based on the payment vehicledata and/or the MID provided to the financial transaction servicesprocessor by the POS.

Once attribution has been determined, various types of reporting can beprovided by the profiler computing system. Such reporting can generallyattribute various online exposure events to subsequent purchase events.The reporting can be anonymized such that personal identifyinginformation is not provided, but the effectiveness of various onlinemarketing efforts can still be gleaned. Additionally, the reporting cansegment or otherwise classify groups of purchasers, purchase events,online activities, or provide other divisions, as may be useful to amerchant, marketer, or other receiving entity. Based on thissegmentation, targeted offers or other forms or marketing can bedirected to particular groups of purchasers, such as purchasers thatvisit particular websites, purchasers who visit particular merchants,purchasers who use particular types of payment vehicles, purchasers whoperform particular online searches, and so forth.

Accordingly, in view of the systems and methods described herein, and asdescribed in more detail below, a purchaser's purchasing activity can beattributed to the purchaser's previous online activity.

FIG. 1 schematically depicts the linkages that can be determined inaccordance with the systems and methods described herein. As depicted bytimes T1-T4, such linkages can be developed over time, as theinformation becomes available. Additionally, while not shown, thelinkages can also be updated over time, as the underlying data maybecome stale or inaccurate. Payment vehicle(s) 120 are first used by apurchaser to make a purchase at an online merchant 140. Such purchasecan be made by network communications between a networked user device160 or computing device of the purchaser and the online merchant 140 orthrough direct communications with the merchant's affiliated financialtransaction services processor. In any event, a link can be establishedbetween the payment vehicle(s) 120 and the networked user device 160.Prior to, or subsequent to, the linking of the payment vehicle(s) 120and the networked user device 160, the purchaser can be exposed toonline exposure data 180 while interacting with the networked userdevice 160. Online exposure data 180 is not intended to be limited toany particular type of data, but rather broadly refers to the wide arrayof information that a purchaser may see, enter, interact with, orotherwise encounter while engaged in online activities. Examples ofonline exposure data 180 can include, without limitation, URLs visited,links that were clicked, keyword searches performed, online shoppingbaskets created, electronic advertisement interaction data, electronicmarketing interaction data, and so forth. At a later point in time, thepurchaser can make a purchase 190 at a merchant 170 using one of thepayment vehicles 120. The merchant 170 can be any type of merchant, suchas a brick-and-mortar retail location or an ecommerce/web-basedmerchant. Based on that purchase event, the purchase 190 at the merchant170 can be linked or attributed to the online exposure data 180, asindicated by attribution 185. Such attribution can be based on, forexample, satisfaction of various rules or guidelines for determiningattribution. Such rules can include time frames, geographicalrestraints, and so forth. In one example, in order for a purchase 190 ata merchant 170 to be attributed to online exposure data 180, thepurchaser must have had to encounter the online exposure data 180 withina certain time frame (e.g., less than 6 months, less than 3 month, lessthan 1 month, less than 1 week, and so forth). In this way, restrictiverules for determining attribution can lead to higher confidence levelsin the attribution 185. The attribution 185 can ultimately be outputtedor otherwise provided to a receiving entity 195. The receiving entity195 can be, for example, the merchant 170, a data aggregator, afinancial transaction services processor, a marketing entity, ananalytics entity, or any other interested party.

FIGS. 2A-2D depict example system diagrams in according with onenon-limiting embodiment, with FIG. 2A schematically depicting profilebuilding, FIG. 2B schematically depicting online tracking, FIG. 2Cschematically depicting purchase tracking, and FIG. 2D schematicallydepicting attribution. Referring first to FIG. 2A, a purchaser 200 isshown that is associated with payment vehicles 202, which are depictedas Payment Vehicles A-N. As is to be appreciated, the payment vehicles202 can include any type of payment vehicle that can be utilized toinitiate a payment transaction. Unless otherwise specified herein,“payment vehicle” includes (1) a physical card including a plastic orpaper card with a magnetic stripe, bar code or other indicatorindicative of an account number or other account indicative information,and/or (2) a virtual card, such as a display or screenshot for a mobilephone or for another portable device (e.g., a flash drive, smart chip, alaptop or portable computer), or for a computer device (e.g., a desktopcomputer) in combination with data indicative of an account number orother account indicative information. Data associated with the cards mayinclude an encrypted or unencrypted account number or other encrypted orunencrypted account indicative information and/or encrypted orunencrypted information associated with a particular card, issuer,creator, or group of merchants. It is also contemplated that the cardmay have multiple embodiments or forms. For example, the card may be aphysical card (e.g., in the form of magnetic striped plastic card), avirtual card (e.g., in the form of a display on a smart phone), or both.In embodiments in which the card is a virtual card, the correspondingaccount information (e.g., account number) would initially be providedto the consumer and the consumer would communicate the accountinformation to the merchant. The virtual card may be communicated bydisplaying a display or screenshot, and/or by transmitting a signal,such as by using NFC (Near Field Communication) technology or othersecure transport technologies to complete the transaction with theselected merchant. Optionally, the virtual card may have a displayelement (e.g., a bar code or string of numbers) which identifies theaccount number associated with the card. Alternatively, the virtual cardmay have display elements relating to the merchants that accept thecard. Thus, whether the card is physical or virtual, it communicatesaccount information.

Still referring to FIG. 2A, the purchaser 200 utilizes a networked userdevice 206 to communicate with one or more online merchants 204 througha communications network 218 (e.g., the Internet, a secure network,etc.). The networked user device 206 can be any suitable computingdevice that facilitates network communications, such as, for example, alaptop computer, a tablet computer, a desktop computer, a smarttelevision, a smart appliance, a mobile computing device, a gamingdevice, a wearable computing device, and so forth. When interacting withthe online merchant 204, the networked user device 206 can be associatedwith a tracking element. The tracking element can be, for example, an IPaddress, a MAC address, a device fingerprint, a browser fingerprint, ora unique identifier associated with the networked user device 206.Additionally or alternatively, the tracking element can be an identifierassociated with the purchaser 200. For example, in some embodiments, thetracking element can be embodied as, or otherwise include, an emailaddress, a postal address, a phone number, a loyalty account number, ausername, and/or any other unique identifier associated with thepurchaser 200. Through a web browser executing on the networked userdevice 206 or through other specialized applications executing on thenetworked user device 206 (sometimes referred to as apps), the purchaser200 can initiate purchase events with one or more of the onlinemerchants 204. The online merchant 204 can present a payment interface(e.g., a payment screen, a POS, etc.) to the purchaser 200 in whichinformation for one or more of the payment vehicles 202 is entered. Thepayment interface can, in turn, communicate with a financial transactionservices processor 220 with appropriate authorization messaging. Thefinancial transaction services processor 220 can communicate withvarious payment networks 230, to seek authorization for the purchaseevent at the online merchant 204. Information based on the transactioncan also be provided to a profiler computing system 222. In someembodiments, the profiler computing system 222 can be a computing systemseparate from the financial transaction services processor 220 andoperated by a separate entity. In other embodiments, the profilercomputing system 222 is a component of the financial transactionservices processor 220 and operated by the same entity, as indicated bydashed box 223. The information provided to the profiler computingsystem 222 can be used to build a profile 226 for the purchaser 200. Theprofile 226 can be stored in a profile data store 224. The profile datastore 224 can be maintained by the profiler computing system 222, as isshown, maintained by the financial transaction services processor 220,or maintained by any other suitable device or entity, such as the dataaggregator computing system 260 (FIG. 2B). The format and content of theprofile 226 can vary, but generally the profile 226 provides a linkageof the payment vehicle(s) 202 used during a purchase event to a trackingelement (e.g., device ID or purchaser ID) of the networked user device206 and/or the purchaser 200. The payment vehicle information as storedin the profile 226 can be tokenized, as may be required by relevant dataprivacy standards. Over time, as the purchaser 200 makes additionalpurchases with the same or different payment vehicles 202 using the sameor different networked user devices 206, the profile 226 can be updatedaccordingly. Furthermore, in some embodiments, the profile 226 canutilize house-holding techniques to link a plurality of differentpurchasers to the same networked user device 206 and/or the samecollection of payment vehicles 202.

Referring now to FIG. 2B, the online activity of the purchaser 200 canbe monitored and logged as part of the attribution techniques describedherein. Using the networked user device 206 (or other computing device),the purchaser 200 can communicate with various web servers 250 over thecommunications network 218. The purchaser 200 can be performing any of avariety of online activities, such as a keyword searching, visiting amerchant's ecommerce website, activating a link on a banneradvertisement, activating a link resulting from a search request, and soforth. Through these interactions, the purchaser 200 is generatingonline exposure data 252 that can be stored by a data aggregatorcomputing system 260. The data aggregator computing system 260 can be aservice provided by a third party, such as Google®, or the dataaggregator computing system 260 can be a component of a merchant'secommerce platform, for example. In some embodiments, the dataaggregator computing system 260 may also receive and store data relatedto the geographical location of purchase event(s) (e.g., “geographicaldata” 208A), data related to the timing of the purchase event(s) (e.g.,“temporal data” 208B), and/or transaction related data received fromthird parties 290 (e.g., stock keeping units and other departments ofmerchants, acquiring banks of merchants, etc.) (“third party data”208C). In any event, the online exposure data 252, geographical data208A, temporal data 208B, and third party data 208C can be linked to, orotherwise associated with, the particular tracking element (e.g., thedevice ID of the networked user device 206 and/or the purchaser ID ofthe purchaser 200) and entered into a tracking profile 270. The trackingprofile 270 can be maintained in a data store 268. In some embodiments,the tracking data maintained by the tracking profile 270 is timestamped. Generally, the data aggregator computing system 260 can monitorand log the purchaser's 200 interactions with various online marketingcampaigns, advertisements, and so forth.

Referring now to FIG. 2C, the purchaser 200 is depicted initiating apurchase event at a brick-and-mortar merchant 280 using their paymentvehicle 202A. While a brick-and-mortar merchant 280 is shown in FIG. 2C,in other embodiments, the merchant 280 can be an online/ecommerce-basedmerchant (e.g., a brick-and-mortar retail location of one of the onlinemerchants 204 illustratively shown in FIG. 2A). A POS of the merchant280 can provide transaction information to the financial transactionservices processor 220 using conventional payment transactioncommunications. The financial transaction services processor 220 canthen communicate with payment networks 230, as may be needed, to seekauthorization for the purchase event. The financial transaction servicesprocessor 220 can also provide information from the purchase event tothe profiler computing system 222. In one embodiment, the information isbased on the payment vehicle 202A. In other embodiments, alternate oradditional information can be provided, such as the name of thepurchaser 200, an email address of the purchaser 200, a postal addressof the purchaser 200, a phone number of the purchaser 200, a username ofthe purchaser 200, and/or a loyalty card number of the purchaser 200. Inuse, the profiler computing system 222 can query the profile data store224 using the information provided by the financial transaction servicesprocessor 220 to determine if a tracking element (e.g., a device ID or apurchaser ID) is affiliated or otherwise associated with theinformation. In the illustrated embodiment, affiliation 228 between“TokenA” and a “Source IP” address is identified in the profile 226.

Referring now to FIG. 2D, the profiler computing system 222 is shownproviding the tracking element (e.g., the device ID or purchaser ID) tothe data aggregator computing system 260 as a query. The data aggregatorcomputing system may also receive data from a third party computingsystem 290. The data aggregator computing system 260 can, in turn, querythe tracking profiles 270 in the data store 268 to determine if onlineexposure data, geographical data, temporal data, third-party data, etc.,are affiliated with that tracking element. It is to be appreciated thatin some embodiments, the tracking profiles 270 can be provided to theprofiler computing system 222 in real-time or batch transfers so thatthe affiliation between a tracking element and any online exposure data,geographical data, temporal data, third-party data, etc., can beperformed by the profiler computing system 222. In other embodiments,the tracking profiles 270 can be provided to the financial transactionservices processor 220 in real-time or batch transfers so that theaffiliation between a tracking element and any online exposure data,geographical data, temporal data, third-party data, etc., can beperformed by the financial transaction services processor 220. In anyevent, the online exposure data, geographical data, temporal data,third-party data, etc., from the tracking profiles 270 can be providedto the profiler computing system 222 for further processing.

As shown in FIG. 3 , an attribution module 227 of the profiler computingsystem 222 can map the online exposure data, geographical data, temporaldata, third-party data, etc. gathered by the data aggregator computingsystem 260 to the purchase event at the brick-and-mortar merchant 280(FIG. 2C). Various rules can be applied by the attribution module 227 todetermine whether to map certain online exposure data, geographicaldata, temporal data, third-party data, etc., to the purchase event. Suchrules can generally impact whether certain online exposure data will belinked to a subsequent purchase event. Once it is determined if apurchase event, or collection of purchase events, can be attributed toonline activity of the purchaser 200, a reporting module 228 can outputan attribution report 280 to a receiving entity 282. The receivingentity 282 can be, without limitation, the merchant 280, the dataaggregator computing system 260, the financial transaction servicesprocessor 220, a marketing/advertisement entity, or any other interestedparty.

In some embodiments, the attribution report 280 can include marketsegmentations or other analytics, as may be useful the receiving entity282. Using the segmentation or other market information provided in theattribution report 280, targeted offers can be provided to the purchaser200, or grouping of purchasers. For example, the targeted offers can bedirected to purchasers who visited particular website, purchasers whohave certain types of payment vehicles, purchasers who visit particularmerchants, or purchasers satisfying other segmentation parameters.

FIG. 4 is an example message sequent chart in accordance with onenon-limiting embodiment. As is to be appreciated, various entities orprocesses illustrated in FIG. 4 can be combined into single entitieswithout departing from the scope of the present disclosure. Furthermore,the particular ordering of the messaging depicted in FIG. 4 is forillustration purposes only. An online purchase 402 is first initiated bya purchaser 450 on a networked user device 452. A purchase event 404 isthen initiated at an online merchant 454. The online merchant 454 canthen communicate with a financial transaction services processor 460 totransmit an authorization request 406 and receive a response 406. Thefinancial transaction services processor 460 can also provide purchaseinformation 408 to a profiler computing system 462. The purchaseinformation 408 can include, for example, a tracking element (e.g., adevice ID of the networked user device 452 or a purchaser ID of thepurchaser 450) to the profiler computing system 462. Additionally, thepurchase information 408 can include payment vehicle information. Theprofiler computing system 462 can then build a purchaser profile 410.

Internet-based browsing 412 can then occur through the networked userdevice 452 engaging in online activity 456. Browsing data 414 can begathered by a data aggregator computing system 464. The data aggregatorcomputing system 464 can build a tracking profile 416 based on theonline activity 456 and/or offline activity based on, e.g., geographicaldata, temporal data, third-party data, etc., and a tracking elementassociated the networked user device 452 (e.g., a device ID) and/or atracking element associated with the purchaser 450 (e.g., a purchaserID).

A subsequent purchase event 418 can then be initiated by the purchaser450 at a brick-and-mortar merchant 458. During that purchase event,authorization communications 420 can occur between a POS of the merchant458 and the financial transaction services processor 460. The financialtransaction services processor 460 can provide purchase information 422to the profiler computing system 462. The purchase information 422 cancomprise, for example, payment vehicle information. The profilercomputing system 462 can use the purchase information 422 to query thepurchaser profiles to identify a purchaser profile 424 of the purchaser450 and determine a particular tracking element (e.g., a device ID, apurchaser ID, etc.) linked or otherwise associated therewith. Thetracking element can then be sent within a query 426 to the dataaggregator computing system 464. The data aggregator computing system464 can respond 428 with a report, which can include online exposuredata, geographical data, temporal data, third-party data, etc., linkedto that tracking element. In some embodiments, the data aggregatorcomputing system 464 can respond with a message indicting that no onlineexposure data, geographical data, temporal data, third-party data, etc.,are linked to the tracking element. The profiler computing system 462can then attribute the subsequent purchase event 418 at thebrick-and-mortar merchant 458 to an online activity 456 and/or anoffline activity based on, e.g., geographical data, temporal data,third-party data, etc., through attribution mapping 430. An attributionreport 430 can then be provided to a receiving entity 466. Method 600,as depicted in FIG. 6 provides a more detailed explanation of at leastsome embodiments of the process of attributing the subsequent purchaseevent 418 to an online activity 456 and/or an offline activity. Forexample, the attribution mapping may include identifying an onlineand/or offline activity that may be attributed to the subsequentpurchase event (e.g., as in step 610 in method 600), and determining anattribution score assessing the strength of attribution between thesubsequent purchase event 418 with the online activity 456 and/oroffline activity (e.g., as in step 612 of method 600).

The processes described above can be performed on or between one or morecomputing devices 500. Referring now to FIG. 5 , an example computingdevice 500 is presented. A computing device 500 can be a server, acomputing device that is integrated with other systems or subsystems, amobile computing device, a cloud-based computing capability, and soforth. The computing device 500 can be any suitable computing device aswould be understood in the art, including without limitation, a customchip, an embedded processing device, a tablet computing device, afinancial transaction services processor, a profiler computing system, adata aggregator computing system, a personal data assistant (PDA), adesktop, a laptop, a microcomputer, a minicomputer, a server, amainframe, or any other suitable programmable device. In variousembodiments disclosed herein, a single component can be replaced bymultiple components and multiple components can be replaced by a singlecomponent to perform a given function or functions. Except where suchsubstitution would not be operative, such substitution is within theintended scope of the embodiments.

The computing device 500 includes a processor 502 that can be anysuitable type of processing unit, for example a general purpose centralprocessing unit (CPU), a reduced instruction set computer (RISC), aprocessor that has a pipeline or multiple processing capabilityincluding having multiple cores, a complex instruction set computer(CISC), a digital signal processor (DSP), an application specificintegrated circuits (ASIC), a programmable logic devices (PLD), and afield programmable gate array (FPGA), among others. The computingresources can also include distributed computing devices, cloudcomputing resources, and virtual computing resources in general.

The computing device 500 also includes one or more memories 506, forexample read only memory (ROM), random access memory (RAM), cache memoryassociated with the processor 502, or other memories such as dynamic RAM(DRAM), static RAM (SRAM), programmable ROM (PROM), electricallyerasable PROM (EEPROM), flash memory, a removable memory card or disk, asolid state drive, and so forth. The computing device 500 also includesstorage media such as a storage device that can be configured to havemultiple modules, such as magnetic disk drives, floppy drives, tapedrives, hard drives, optical drives and media, magneto-optical drivesand media, compact disc drives, Compact Disc Read Only Memory (CDROM),Compact Disc Recordable (CD-R), Compact Disc Rewriteable (CD-RW), asuitable type of Digital Versatile Disc (DVD) or BluRay disc, and soforth. Storage media such as flash drives, solid state hard drives,redundant array of individual disks (RAID), virtual drives, networkeddrives and other memory means including storage media on the processor502, or memories 506 are also contemplated as storage devices. It can beappreciated that such memory can be internal or external with respect tooperation of the disclosed embodiments. It can be appreciated thatcertain portions of the processes described herein can be performedusing instructions stored on a computer-readable medium or media thatdirect a computer system to perform the process steps. Non-transitorycomputer-readable media, as used herein, comprises all computer-readablemedia except for transitory, propagating signals.

Network and communication interfaces 512 can be configured to transmitto, or receive data from, other computing devices 500 across a network514. The network and communication interfaces 512 can be an Ethernetinterface, a radio interface, a Universal Serial Bus (USB) interface, orany other suitable communications interface and can include receivers,transmitter, and transceivers. For purposes of clarity, a transceivercan be referred to as a receiver or a transmitter when referring to onlythe input or only the output functionality of the transceiver. Examplecommunication interfaces 512 can include wired data transmission linkssuch as Ethernet and TCP/IP. The communication interfaces 512 caninclude wireless protocols for interfacing with private or publicnetworks 514. For example, the network and communication interfaces 512and protocols can include interfaces for communicating with privatewireless networks such as a Wi-Fi network, one of the IEEE 802.11xfamily of networks, or another suitable wireless network. The networkand communication interfaces 512 can include interfaces and protocolsfor communicating with public wireless networks 512, using for examplewireless protocols used by cellular network providers, including CodeDivision Multiple Access (CDMA) and Global System for MobileCommunications (GSM). The computing device 500 can use network andcommunication interfaces 512 to communicate with hardware modules suchas a database or data store, or one or more servers or other networkedcomputing resources. Data can be encrypted or protected fromunauthorized access.

In various configurations, the computing device 500 can include a systembus 516 for interconnecting the various components of the computingdevice 500, or the computing device 500 can be integrated into one ormore chips such as programmable logic device or application specificintegrated circuit (ASIC). The system bus 516 can include a memorycontroller, a local bus, or a peripheral bus for supporting input andoutput devices 504, and communication interfaces 512. Example input andoutput devices 504 include keyboards, keypads, gesture or graphicalinput devices, motion input devices, touchscreen interfaces, one or moredisplays, audio units, voice recognition units, vibratory devices,computer mice, and any other suitable user interface or device forreceiving user inputs and/or outputting information.

The processor 502 and memory 506 can include nonvolatile memory forstoring computer-readable instructions, data, data structures, programmodules, code, microcode, and other software components for storing thecomputer-readable instructions in non-transitory computer-readablemediums in connection with the other hardware components for carryingout the methodologies described herein. Software components can includesource code, compiled code, interpreted code, executable code, staticcode, dynamic code, encrypted code, or any other suitable type of codeor computer instructions implemented using any suitable high-level, lowlevel, object-oriented, visual, compiled, or interpreted programminglanguage.

FIG. 6 is a flow chart depicting an example process 600 for attributinga purchase event with an identified activity. Process 600 may beperformed by one or more of financial transaction services processor220, profiler computing system 222, and/or data aggregator computingsystem 260.

Step 602 may include receiving purchase information associated with apurchase event. The purchase information may include transactionalinformation, e.g., past transaction amounts, merchant identifiers (MID)of transactions, and/or data related to or identifying the paymentmethods and/or payment vehicles used in transactions, among otherinformation. In some embodiments, the purchase information may include atracking element (e.g., a device ID of the networked user device 452 orcomputing system of the purchaser, or a purchaser ID of the purchaser450) to the profiler computing system 462.

Step 604 may include identifying the purchase profile of a purchaserbased on the purchase information. For example, the financialtransaction services processor 460 can provide the received purchaseinformation 422 to the profiler computing system 462. The purchaseinformation 422 can comprise, for example, payment vehicle information.The profiler computing system 462 can use the purchase information 422to query the purchaser profiles stored within the profiler computingsystem 462 to identify a purchaser profile 424 of the purchaser 450.

In some embodiments, step 606 may include identifying the trackingprofile of the purchaser based on the purchase profile. In suchembodiments, the purchase information and/or the purchase profile mayinclude a tracking element (e.g., a device ID, a purchaser ID, etc.).When the profiler computing system 462 uses the purchase information 422to query the purchaser profiles stored within the profiler computingsystem 462 to identify a purchaser profile 424 of the purchaser 450, theprofiler computing system 462 may also determine a particular trackingelement (e.g., a device ID, a purchaser ID, etc.) linked or otherwiseassociated with the purchaser profile. The tracking element can then besent within a query 426 to the data aggregator computing system 464. Thedata aggregator computing system 464 can respond 428 with a trackingprofile. In some embodiments, the tracking profile may be a report,which may include online exposure data linked to the tracking element.As will be described below, the profiler computing system 462 mayattribute a purchase event to an online activity 456 (or an activityassociated with one or more environmental and/or behavioral data). Thestrength of the attribution may be based on an attribution score. Anattribution report 430 may include such an attribution score and may beprovided to a receiving entity 466.

Step 608 may include receiving environmental and/or behavioral dataassociated with the current and prior purchase events, based on thepurchase profile and/or the tracking profile. In various embodiments,environmental and/or behavioral data associated with a purchase eventmay refer to, for example, characteristics related to the geographicallocation of purchase event(s) (e.g., “geographical data” 608A),characteristics related to the timing of the purchase event(s) (e.g.,“temporal data” 608B), online exposure data of the purchaser 608C (e.g.,as may be received from the tracking profile identified in step 606),fingerprint data associated with the networked user device 160 of thepurchaser (e.g., “device fingerprint data” 608D), or third party data608E.

The environmental and/or behavioral data associated with purchase eventsmay be used to attribute purchaser's events to specific purchaseractivities. e.g., using the data aggregator computing system 260.Moreover, in addition to online activity of the purchaser beingmonitored and logged as part of the attribution techniques described inFIG. 2B, environmental and/or behavioral data associated with purchaseevents of the purchaser may also be monitored and logged. Thus, thetracking profile may include, for example, geographical data regardingthe location of the purchaser or the networked user device of thepurchaser, temporal data regarding a purchase event, as well as thirdparty data received from third parties. The tracking profile 270 can bemaintained in a data store 268. In some embodiments, the tracking datamaintained by the tracking profile 270 is time stamped. Generally, thedata aggregator computing system 260 can monitor and log the purchaser's200 interactions with various online marketing campaigns,advertisements, and so forth.

One form of environmental and/or behavioral data that may be used toattribute purchaser's events to specific purchaser activities isgeographical data 608A. Through the understanding of consumer location,via both enhanced consumer profiling and prior offline purchaseactivity, the accuracy of attribution may be greatly increased as theconfidence of specific consumer assignment to online activity isincreased. Furthermore, geolocation and offline purchase activityattribution modeling may be better able to address transient behavior.

Another form of environmental and/or behavioral data that may be used toattribute purchaser's events to specific purchaser activities istemporal data 608B, which may include, for example, the duration of apurchase event, the time and date in which the purchase event wasinitiated (e.g., the purchaser begins browsing items at a store), and/orthe time and date in which the transaction occurred, among other things.In some embodiments, online or mobile transactions may or may not have adurational component, as they are not constrained by typical retailhours. In such embodiments, other temporal data may be taken intoaccount, (including time at which the transaction occurs).

Another form of environmental and/or behavioral data that may be used toattribute purchaser's events to specific purchaser activities is devicefingerprinting data 608D, which may enable the identification andtracking of multiple networked user devices and/or computing systems.The networked user device and/or computer system of the purchaser may beidentified and/or tracked using, for example, an IP address, an OSconfiguration, a static HTIP, browser and plug-in variables (balancingdiversity and stability). Once the networked user device and/orcomputing systems of the purchaser have been identified and the onlineactivities of the purchaser have been tracked, the networked userdevices and/or computing systems may be associated with the purchaserprofiles and/or profile computing system. The purchaser profile and/orprofile computing system can be linked to the one or more paymentmethods and/or payment networks that the purchaser uses during purchaseevents. Device fingerprinting may allow for broader coverage and moreaccurate attribution of an activity to a purchase event as any givenconsumer can be appropriately associated with multiple devices and thenassociated with one or more payment methods and/or payment networks usedat the purchase events. Thus, device fingerprinting may allow for abetter correlation between online activity and offline purchaseactivity, e.g., via a preferred payment method and/or payment network.

Another form of environmental and/or behavioral data that may be used toattribute purchase events to specific purchaser activities is thirdparty data 608E. Third party data may include, for example, purchaseinformation for one or more purchase events, and may be received from amerchant, merchant department, a Stock Keeping Unit (SKU), etc. Thethird party data may allow for greater accuracy in attributing purchaseevents to specific purchaser activities. Such an embodiment requirespartnership with third parties either to provide license to this data orthe data itself to further enhance models.

Step 610 may include identifying an activity that may be attributed tothe purchase event. The activities may include, for example, an exposureor engagement with an advertisement, a receipt of a marketing orpromotional material, online activity, an entry into the premises of amerchant, the visibility of a merchant to a purchaser, among others.

Step 612 may include determining an attribution score assessing thestrength of the attribution between the identified activity and thepurchase event. The various environmental and/or behavioral data may beused, individually or in combination, to determine the attributionscore. For example, the time between the online ad delivery to thepurchaser's networked user device or the (purchaser's engagement withthe delivered ad) and the actual offline purchase may yield a greaterconfidence in the attribution of the online ad delivery or engagementwith the purchase, and may therefore yield a higher attribution score.Additionally, if between the online ad and the targeted purchase, thepurchaser was exposed to other ads or performed other purchases withinthe same merchant category, the attribution score between the originalonline ad and purchase may be lower. Similarly, as time between anonline activity and offline purchase increases, confidence in theattribution of the online activity to the offline purchase may alsodecrease, yielding a lower attribution score.

In some embodiments, additionally or alternatively, an attribution modelmay be generated. The attribution model may represent the strength ofattribution of various one or more activities to a purchase event. Inone embodiment, the attribution model may quantify the “halo” effect ofonline advertising. The halo effect may describe the unintended liftretailers may see base on a competitor's ad(s). The presently-disclosedtechniques are as applicable to attribution and quantification of thisHalo effect. In fact, sizing the halo effect may be an additional leverfor retailers in ad optimization, e.g., by looking at the white spacepresented via the halo effect and tweaking ads to both maximize ad ROIwhile minimizing halo impact. In various embodiments, a halo effect mayrefer to the increase in sales of a good, service, and/or stock keepingunit of a merchant that is in a similar merchant category but isdifferent from a merchant from whom a purchaser may have received orhave been exposed to (e.g., by way of online marketing). In suchembodiments, given extensive visibility in offline retail purchaseactivity, the attribution model may be used to measure the ‘halo’ effector lift in sales and/or that a merchant, in a similar category, mightsee from a competitor's online ad.

The “halo” effect may be described in the following example. A purchaserreceives or is exposed to an online ad promoting a certain category of amerchant (e.g., consumer electronics merchants) and/or a stock keepingunit SKU (e.g., a 4K HDTV). The ad itself may have been generated or betargeted towards purchasing from a specific merchant (e.g., “Retailer1”). Subsequently, received purchase information or environmental and/orbehavioral data indicates that the purchaser did in fact make a consumerelectronics purchase (e.g., a 4K HDTV), but did not purchase it atRetailer 1. The received purchase information or environmental and/orbehavioral data may indicate, however, that the purchaser made thepurchase at a different consumer electronics merchant. In that scenario,there may be a correlation between the ad presentment and a subsequentpurchase, which could indicate a leakage in advertising spend, forexample.

In some embodiments, the profiler computing system 222, data aggregatorcomputing system 260, data aggregator computing system 260, financialtransaction services processor 220 (or computing devices thereof),payment networks 230 (or computing devices thereof), networked userdevice 206, online merchant 204 (or computing devices thereof), brickand mortar merchant 280 (or computing devices thereof), receiving entity282 (or computing devices thereof), and web servers 250 can eachestablish an environment during operation. Each environment can includevarious modules, components, sub-components, and devices commonly foundin computing devices, which are not illustrated in the figures forclarity of the description. The various modules, components,sub-components, and devices of each environment can be embodied ashardware, firmware, software, or a combination thereof. For example, oneor more of the modules, components, sub-components, and devices of eachenvironment can be embodied as a processor and/or a controllerconfigured to provide the functionality described herein.

These and other embodiments of the systems and methods can be used aswould be recognized by those skilled in the art. The above descriptionsof various systems and methods are intended to illustrate specificexamples and describe certain ways of making and using the systemsdisclosed and described here. These descriptions are neither intended tobe nor should be taken as an exhaustive list of the possible ways inwhich these systems can be made and used. A number of modifications,including substitutions of systems between or among examples andvariations among combinations can be made. Those modifications andvariations should be apparent to those of ordinary skill in this areaafter having read this disclosure.

What is claimed is:
 1. A method of attributing payment vehicle purchaseevents to activity, the method comprising: receiving, by a profilercomputing system by way of a networked user device, purchase informationassociated with a completed purchase event of a purchaser using apayment vehicle; identifying, by a processor of the profiler computingsystem, a purchaser profile associated with the purchaser, the purchaserprofile comprising payment vehicle data and a tracking element andwherein the identifying the purchaser profile comprises: accessing,using the processor of the profiler computing system, a database of theprofiler computer system, wherein the database comprises a plurality ofpurchaser profiles and wherein each of the plurality of purchaserprofiles comprises payment vehicle data and tracking element data;comparing, using the processor of the profiler computing system, thepayment vehicle associated with the completed purchase event to thepayment vehicle data of each of the plurality of purchaser profiles; andidentifying, using the processor of the profiler computing system, thepurchaser profile by identifying a match between the payment vehicle andthe payment vehicle data of one of the plurality of purchaser profiles;generating, by an electronic transaction processor, one or more paymentvehicle tokens based on the payment vehicle data; affiliating, by theprocessor of the profiler computing system, the tracking element of theidentified purchaser profile to the one or more of payment vehicletokens; generating, by the processor of the profiler computing systemand based on data obtained from the tracking element affiliated with theone or more payment vehicle tokens, an attribution report thatidentifies that the completed purchase event is attributed to anon-purchase activity of the purchaser, wherein the non-purchaseactivity comprises interaction of the purchaser with an advertisementpromoting a product category of a merchant; and quantifying, based onanalysis of the attribution report by the processor of the profilercomputing system, an influential effect of the advertisement, whereinthe quantifying comprises: identifying, using the processor of theprofiler computing system, that the completed purchase eventcorresponded to a product associated with the product category sold byanother merchant; and determining, using the processor of the profilercomputing system and based on attribution report, that the advertisementfacilitated the completed purchase event of the product.
 2. The methodof claim 1, the method further comprising: receiving, by the profilercomputing system, initial online purchase information associated with aninitial online completed purchase event of the purchaser; generating, bythe profiler computing system, the purchaser profile associated with thepurchaser based on the initial online purchase information; storing, bythe profiler computing system, the generated purchaser profileassociated with the purchaser in the profile data store; anddetermining, by the processor of the profiler computing system, thestrength of attributing the completed purchase event to the non-purchaseactivity of the purchaser based on the initial online purchaseinformation.
 3. The method of claim 1, further comprising: requesting,by the profiler computing system and from a data aggregator computingsystem, environmental and/or behavioral data associated with thetracking element of the identified purchase profile; and receiving, bythe profiler computing system and from the data aggregator computingsystem, the environmental and/or behavioral data associated with thetracking element of the identified purchase profile.
 4. The method ofclaim 3, wherein the environmental and/or behavioral data received fromthe data aggregator computing system comprises one or more of:geographical data of the purchaser; temporal data of the purchaser;online activity tracking data and electronic advertisement interactiondata; and geographical, temporal, or online activity tracking data orelectronic advertisement interaction data received from a third party.5. The method of claim 3, wherein the environmental and/or behavioraldata received from the data aggregator computing system comprises amessage indicating that previous environmental and/or behavioral dataassociated with the tracking element is nonexistent.
 6. The method ofclaim 3, wherein requesting the environmental and/or behavioral datacomprises transmitting the tracking element to the data aggregatorcomputing system.
 7. The method of claim 1, further comprising:analyzing, by the profiler computing system, the non-purchase activityof the purchaser as a function of environmental and/or behavioral data.8. The method of claim 1, further comprising: determining, by theprofiler computing system, an attribution score based on the strength ofattributing completed purchase event to the non-purchase activity of thepurchaser; and determining whether to attribute the completed purchaseevent to the non-purchase activities of the purchaser based ondetermining whether the attribution score is above a predeterminingthreshold.
 9. A system for attributing payment vehicle purchase eventsto previous activity of a purchaser, the system comprising: a profilercomputing device comprising a processor executing instructions stored inmemory, wherein the instructions cause the processor to: receive, by aprofiler computing system by way of a networked user device, purchaseinformation associated with a completed purchase event of a purchaserusing a payment vehicle; identify, by a processor of the profilercomputing system, a purchaser profile associated with the purchaser, thepurchaser profile comprising payment vehicle data and a tracking elementand wherein the identifying the purchaser profile comprises: accessing,using the processor of the profiler computing system, a database of theprofiler computer system, wherein the database comprises a plurality ofpurchaser profiles and wherein each of the plurality of purchaserprofiles comprises payment vehicle data and tracking element data;comparing, using the processor of the profiler computing system, thepayment vehicle associated with the completed purchase event to thepayment vehicle data of each of the plurality of purchaser profiles; andidentifying, using the processor of the profiler computing system, thepurchaser profile by identifying a match between the payment vehicle andthe payment vehicle data of one of the plurality of purchaser profiles;generate, by an electronic transaction processor, one or more paymentvehicle tokens based on the payment vehicle data; affiliate, by theprocessor of the profiler computing system, the tracking element of theidentified purchaser profile to the one or more of payment vehicletokens; generate, by the processor of the profiler computing system andbased on data obtained from the tracking element affiliated with the oneor more payment vehicle tokens, an attribution report that identifiesthat the completed purchase event is attributed to a non-purchaseactivity of the purchaser, wherein the non-purchase activity comprisesinteraction of the purchaser with an advertisement promoting a productcategory of a merchant; and quantify, based on analysis of theattribution report by the processor of the profiler computing system, aninfluential effect of the advertisement, wherein the quantifyingcomprises: identifying, using the processor of the profiler computingsystem, that the completed purchase event corresponded to a productassociated with the product category sold by another merchant; anddetermining, using the processor of the profiler computing system andbased on attribution report, that the advertisement facilitated thecompleted purchase event of the product.
 10. The system of claim 9,wherein the instructions further cause the processor to: receive, by theprofiler computing system, initial online purchase informationassociated with an initial online completed purchase event of thepurchaser; generate, by the profiler computing system, the purchaserprofile associated with the purchaser based on the initial onlinepurchase information; store, by the profiler computing system, thegenerated purchaser profile associated with the purchaser in the profiledata store; and determine, by the processor of the profiler computingsystem, the strength of attributing the completed purchase event to thenon-purchase activity of the purchaser based on the initial onlinepurchase information.
 11. The system of claim 9, wherein theinstructions further cause the processor to: request, by the profilercomputing system and from a data aggregator computing system,environmental and/or behavioral data associated with the trackingelement of the identified purchase profile; and receive, by the profilercomputing system and from the data aggregator computing system, theenvironmental and/or behavioral data associated with the trackingelement of the identified purchase profile.
 12. The system of claim 11,wherein the environmental and/or behavioral data received from the dataaggregator computing system comprises one or more of: geographical dataof the purchaser; temporal data of the purchaser; online activitytracking data and electronic advertisement interaction data; andgeographical, temporal, or online activity tracking data or electronicadvertisement interaction data received from a third party.
 13. Thesystem of claim 11, wherein the environmental and/or behavioral datareceived from the data aggregator computing system comprises a messageindicating that previous environmental and/or behavioral data associatedwith the tracking element is nonexistent.
 14. The system of claim 11,wherein requesting the environmental and/or behavioral data comprisestransmitting the tracking element to the data aggregator computingsystem.
 15. The system of claim 9, wherein the instructions furthercause the processor to: analyze, by the profiler computing system, thenon-purchase activity of the purchaser as a function of environmentaland/or behavioral data.
 16. The system of claim 9, wherein theinstructions further cause the processor to: determine, by the profilercomputing system, an attribution score based on the strength ofattributing completed purchase event to the non-purchase activity of thepurchaser; and determine whether to attribute the completed purchaseevent to the non-purchase activities of the purchaser based ondetermining whether the attribution score is above a predeterminingthreshold.
 17. A non-transitory machine-readable medium storesinstructions that, when executed by a profiler computing system, causesthe profiler computing system to perform a method for attributingpayment vehicle purchase events to previous activity of a purchaser, themethod comprising: receiving, by a profiler computing system by way of anetworked user device, purchase information associated with a completedpurchase event of a purchaser using a payment vehicle; identifying, by aprocessor of the profiler computing system, a purchaser profileassociated with the purchaser, the purchaser profile comprising paymentvehicle data and a tracking element and wherein the identifying thepurchaser profile comprises: accessing, using the processor of theprofiler computing system, a database of the profiler computer system,wherein the database comprises a plurality of purchaser profiles andwherein each of the plurality of purchaser profiles comprises paymentvehicle data and tracking element data; comparing, using the processorof the profiler computing system, the payment vehicle associated withthe completed purchase event to the payment vehicle data of each of theplurality of purchaser profiles; and identifying, using the processor ofthe profiler computing system, the purchaser profile by identifying amatch between the payment vehicle and the payment vehicle data of one ofthe plurality of purchaser profiles; generating, by an electronictransaction processor, one or more payment vehicle tokens based on thepayment vehicle data; affiliating, by the processor of the profilercomputing system, the tracking element of the identified purchaserprofile to the one or more of payment vehicle tokens; generating, by theprocessor of the profiler computing system and based on data obtainedfrom the tracking element affiliated with the one or more paymentvehicle tokens, an attribution report that identifies that the completedpurchase event is attributed to a non-purchase activity of thepurchaser, wherein the non-purchase activity comprises interaction ofthe purchaser with an advertisement promoting a product category of amerchant; and quantifying, based on analysis of the attribution reportby the processor of the profiler computing system, an influential effectof the advertisement, wherein the quantifying comprises: identifying,using the processor of the profiler computing system, that the completedpurchase event corresponded to a product associated with the productcategory sold by another merchant; and determining, using the processorof the profiler computing system and based on attribution report, thatthe advertisement facilitated the completed purchase event of theproduct.
 18. The non-transitory machine-readable medium of claim 17, themethod further comprising: receiving, by the profiler computing system,initial online purchase information associated with an initial onlinecompleted purchase event of the purchaser; generating, by the profilercomputing system, the purchaser profile associated with the purchaserbased on the initial online purchase information; storing, by theprofiler computing system, the generated purchaser profile associatedwith the purchaser in the profile data store; determining, by theprocessor of the profiler computing system, the strength of attributingthe completed purchase event to the non-purchase activity of thepurchaser based on the initial online purchase information; analyzing,by the profiler computing system, the non-purchase activity of thepurchaser as a function of environmental and/or behavioral data;determining, by the profiler computing system, an attribution scorebased on the strength of attributing completed purchase event to thenon-purchase activity of the purchaser; and determining whether toattribute the completed purchase event to the non-purchase activities ofthe purchaser based on determining whether the attribution score isabove a predetermining threshold.
 19. The non-transitorymachine-readable medium of claim 17, the method further comprising:requesting, by the profiler computing system and from a data aggregatorcomputing system, environmental and/or behavioral data associated withthe tracking element of the identified purchase profile; receiving, bythe profiler computing system and from the data aggregator computingsystem, the environmental and/or behavioral data associated with thetracking element of the identified purchase profile; and requesting theenvironmental and/or behavioral data comprises transmitting the trackingelement to the data aggregator computing system.
 20. The non-transitorymachine-readable medium of claim 17, wherein the environmental and/orbehavioral data received from the data aggregator computing systemcomprises one or more of: geographical data of the purchaser; temporaldata of the purchaser; online activity tracking data and electronicadvertisement interaction data; geographical, temporal, or onlineactivity tracking data or electronic advertisement interaction datareceived from a third party; and a message indicating that previousenvironmental and/or behavioral data associated with the trackingelement is nonexistent.